Overview

Dataset statistics

Number of variables22
Number of observations1311
Missing cells1065
Missing cells (%)3.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory225.5 KiB
Average record size in memory176.1 B

Variable types

Categorical3
Numeric19

Warnings

Producer has a high cardinality: 675 distinct values High cardinality
altitude_low_meters is highly correlated with altitude_high_meters and 1 other fieldsHigh correlation
altitude_high_meters is highly correlated with altitude_low_meters and 1 other fieldsHigh correlation
altitude_mean_meters is highly correlated with altitude_low_meters and 1 other fieldsHigh correlation
Producer has 230 (17.5%) missing values Missing
Processing Method has 152 (11.6%) missing values Missing
altitude_low_meters has 227 (17.3%) missing values Missing
altitude_high_meters has 227 (17.3%) missing values Missing
altitude_mean_meters has 227 (17.3%) missing values Missing
altitude_low_meters is highly skewed (γ1 = 20.0978744) Skewed
altitude_high_meters is highly skewed (γ1 = 20.08565748) Skewed
altitude_mean_meters is highly skewed (γ1 = 20.09518078) Skewed
Moisture has 252 (19.2%) zeros Zeros
Category.One.Defects has 1111 (84.7%) zeros Zeros
Quakers has 1216 (92.8%) zeros Zeros
Category.Two.Defects has 362 (27.6%) zeros Zeros

Reproduction

Analysis started2021-02-14 13:11:01.139960
Analysis finished2021-02-14 13:11:42.398674
Duration41.26 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

Distinct36
Distinct (%)2.7%
Missing1
Missing (%)0.1%
Memory size10.4 KiB
Mexico
236 
Colombia
183 
Guatemala
181 
Brazil
132 
Taiwan
75 
Other values (31)
503 

Length

Max length28
Median length8
Mean length8.893129771
Min length4

Characters and Unicode

Total characters11650
Distinct characters47
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.6%

Sample

1st rowEthiopia
2nd rowEthiopia
3rd rowGuatemala
4th rowEthiopia
5th rowEthiopia
ValueCountFrequency (%)
Mexico236
18.0%
Colombia183
14.0%
Guatemala181
13.8%
Brazil132
10.1%
Taiwan75
 
5.7%
United States (Hawaii)73
 
5.6%
Honduras53
 
4.0%
Costa Rica51
 
3.9%
Ethiopia44
 
3.4%
Tanzania, United Republic Of40
 
3.1%
Other values (26)242
18.5%
2021-02-14T14:11:42.599978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mexico236
14.1%
colombia183
 
11.0%
guatemala181
 
10.8%
brazil132
 
7.9%
united125
 
7.5%
states85
 
5.1%
taiwan75
 
4.5%
hawaii73
 
4.4%
honduras53
 
3.2%
rica51
 
3.1%
Other values (35)477
28.5%

Most occurring characters

ValueCountFrequency (%)
a1933
16.6%
i1267
 
10.9%
o805
 
6.9%
e742
 
6.4%
l626
 
5.4%
t590
 
5.1%
n502
 
4.3%
m384
 
3.3%
361
 
3.1%
c358
 
3.1%
Other values (37)4082
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9423
80.9%
Uppercase Letter1671
 
14.3%
Space Separator361
 
3.1%
Open Punctuation77
 
0.7%
Close Punctuation77
 
0.7%
Other Punctuation41
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
a1933
20.5%
i1267
13.4%
o805
8.5%
e742
 
7.9%
l626
 
6.6%
t590
 
6.3%
n502
 
5.3%
m384
 
4.1%
c358
 
3.8%
u323
 
3.4%
Other values (13)1893
20.1%
ValueCountFrequency (%)
M256
15.3%
C251
15.0%
G182
10.9%
U151
9.0%
T147
8.8%
B134
8.0%
H132
7.9%
S106
6.3%
R96
 
5.7%
E66
 
3.9%
Other values (9)150
9.0%
ValueCountFrequency (%)
,40
97.6%
?1
 
2.4%
ValueCountFrequency (%)
361
100.0%
ValueCountFrequency (%)
(77
100.0%
ValueCountFrequency (%)
)77
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11094
95.2%
Common556
 
4.8%

Most frequent character per script

ValueCountFrequency (%)
a1933
17.4%
i1267
 
11.4%
o805
 
7.3%
e742
 
6.7%
l626
 
5.6%
t590
 
5.3%
n502
 
4.5%
m384
 
3.5%
c358
 
3.2%
u323
 
2.9%
Other values (32)3564
32.1%
ValueCountFrequency (%)
361
64.9%
(77
 
13.8%
)77
 
13.8%
,40
 
7.2%
?1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11650
100.0%

Most frequent character per block

ValueCountFrequency (%)
a1933
16.6%
i1267
 
10.9%
o805
 
6.9%
e742
 
6.4%
l626
 
5.4%
t590
 
5.1%
n502
 
4.3%
m384
 
3.3%
361
 
3.1%
c358
 
3.1%
Other values (37)4082
35.0%

Producer
Categorical

HIGH CARDINALITY
MISSING

Distinct675
Distinct (%)62.4%
Missing230
Missing (%)17.5%
Memory size10.4 KiB
La Plata
 
30
Ipanema Agrícola SA
 
22
Doi Tung Development Project
 
17
Ipanema Agricola
 
12
VARIOS
 
12
Other values (670)
988 

Length

Max length100
Median length19
Mean length20.54024052
Min length1

Characters and Unicode

Total characters22204
Distinct characters222
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique526 ?
Unique (%)48.7%

Sample

1st rowMETAD PLC
2nd rowMETAD PLC
3rd rowYidnekachew Dabessa Coffee Plantation
4th rowMETAD PLC
5th rowHVC
ValueCountFrequency (%)
La Plata30
 
2.3%
Ipanema Agrícola SA22
 
1.7%
Doi Tung Development Project17
 
1.3%
Ipanema Agricola12
 
0.9%
VARIOS12
 
0.9%
Ipanema Agricola S.A11
 
0.8%
ROBERTO MONTERROSO10
 
0.8%
LA PLATA9
 
0.7%
AMILCAR LAPOLA9
 
0.7%
Reinerio Zepeda9
 
0.7%
Other values (665)940
71.7%
(Missing)230
 
17.5%
2021-02-14T14:11:42.847340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de86
 
2.5%
65
 
1.9%
coffee64
 
1.9%
la60
 
1.8%
s.a50
 
1.5%
ipanema50
 
1.5%
plata41
 
1.2%
agricola37
 
1.1%
ltd33
 
1.0%
sa30
 
0.9%
Other values (1241)2902
84.9%

Most occurring characters

ValueCountFrequency (%)
2392
 
10.8%
A1473
 
6.6%
a1174
 
5.3%
R962
 
4.3%
E961
 
4.3%
e902
 
4.1%
O886
 
4.0%
o824
 
3.7%
I758
 
3.4%
L674
 
3.0%
Other values (212)11198
50.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10990
49.5%
Lowercase Letter8057
36.3%
Space Separator2392
 
10.8%
Other Punctuation373
 
1.7%
Other Letter233
 
1.0%
Decimal Number99
 
0.4%
Dash Punctuation17
 
0.1%
Open Punctuation14
 
0.1%
Close Punctuation14
 
0.1%
Math Symbol12
 
0.1%
Other values (2)3
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
12
 
5.2%
7
 
3.0%
6
 
2.6%
5
 
2.1%
5
 
2.1%
5
 
2.1%
5
 
2.1%
4
 
1.7%
4
 
1.7%
4
 
1.7%
Other values (122)176
75.5%
ValueCountFrequency (%)
a1174
14.6%
e902
11.2%
o824
10.2%
n608
 
7.5%
r607
 
7.5%
i554
 
6.9%
l392
 
4.9%
t392
 
4.9%
s328
 
4.1%
u322
 
4.0%
Other values (24)1954
24.3%
ValueCountFrequency (%)
A1473
13.4%
R962
 
8.8%
E961
 
8.7%
O886
 
8.1%
I758
 
6.9%
L674
 
6.1%
N620
 
5.6%
S610
 
5.6%
C601
 
5.5%
M430
 
3.9%
Other values (22)3015
27.4%
ValueCountFrequency (%)
020
20.2%
220
20.2%
116
16.2%
312
12.1%
911
11.1%
48
 
8.1%
65
 
5.1%
73
 
3.0%
52
 
2.0%
82
 
2.0%
ValueCountFrequency (%)
.195
52.3%
,95
25.5%
/53
 
14.2%
&13
 
3.5%
'6
 
1.6%
:6
 
1.6%
;5
 
1.3%
ValueCountFrequency (%)
2392
100.0%
ValueCountFrequency (%)
|12
100.0%
ValueCountFrequency (%)
-17
100.0%
ValueCountFrequency (%)
(14
100.0%
ValueCountFrequency (%)
)14
100.0%
ValueCountFrequency (%)
2
100.0%
ValueCountFrequency (%)
_1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin19047
85.8%
Common2924
 
13.2%
Han233
 
1.0%

Most frequent character per script

ValueCountFrequency (%)
12
 
5.2%
7
 
3.0%
6
 
2.6%
5
 
2.1%
5
 
2.1%
5
 
2.1%
5
 
2.1%
4
 
1.7%
4
 
1.7%
4
 
1.7%
Other values (122)176
75.5%
ValueCountFrequency (%)
A1473
 
7.7%
a1174
 
6.2%
R962
 
5.1%
E961
 
5.0%
e902
 
4.7%
O886
 
4.7%
o824
 
4.3%
I758
 
4.0%
L674
 
3.5%
N620
 
3.3%
Other values (56)9813
51.5%
ValueCountFrequency (%)
2392
81.8%
.195
 
6.7%
,95
 
3.2%
/53
 
1.8%
020
 
0.7%
220
 
0.7%
-17
 
0.6%
116
 
0.5%
(14
 
0.5%
)14
 
0.5%
Other values (14)88
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII21867
98.5%
CJK233
 
1.0%
None102
 
0.5%
Punctuation2
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
2392
 
10.9%
A1473
 
6.7%
a1174
 
5.4%
R962
 
4.4%
E961
 
4.4%
e902
 
4.1%
O886
 
4.1%
o824
 
3.8%
I758
 
3.5%
L674
 
3.1%
Other values (65)10861
49.7%
ValueCountFrequency (%)
í25
24.5%
é13
12.7%
ó12
11.8%
Ñ10
 
9.8%
Í9
 
8.8%
ú7
 
6.9%
á6
 
5.9%
Ó4
 
3.9%
É4
 
3.9%
è4
 
3.9%
Other values (4)8
 
7.8%
ValueCountFrequency (%)
12
 
5.2%
7
 
3.0%
6
 
2.6%
5
 
2.1%
5
 
2.1%
5
 
2.1%
5
 
2.1%
4
 
1.7%
4
 
1.7%
4
 
1.7%
Other values (122)176
75.5%
ValueCountFrequency (%)
2
100.0%

Number.of.Bags
Real number (ℝ≥0)

Distinct130
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.8878719
Minimum0
Maximum1062
Zeros1
Zeros (%)0.1%
Memory size10.4 KiB
2021-02-14T14:11:43.334280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q114.5
median175
Q3275
95-th percentile320
Maximum1062
Range1062
Interquartile range (IQR)260.5

Descriptive statistics

Standard deviation129.7337335
Coefficient of variation (CV)0.8430406631
Kurtosis0.2831356219
Mean153.8878719
Median Absolute Deviation (MAD)125
Skewness0.3235951091
Sum201747
Variance16830.84162
MonotocityNot monotonic
2021-02-14T14:11:43.456260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250241
18.4%
275176
13.4%
10108
 
8.2%
187
 
6.6%
30071
 
5.4%
32070
 
5.3%
5042
 
3.2%
10037
 
2.8%
2035
 
2.7%
229
 
2.2%
Other values (120)415
31.7%
ValueCountFrequency (%)
01
 
0.1%
187
6.6%
229
 
2.2%
318
 
1.4%
46
 
0.5%
ValueCountFrequency (%)
10621
0.1%
6001
0.1%
5502
0.2%
5002
0.2%
4502
0.2%

Processing Method
Categorical

MISSING

Distinct5
Distinct (%)0.4%
Missing152
Missing (%)11.6%
Memory size10.4 KiB
Washed / Wet
812 
Natural / Dry
251 
Semi-washed / Semi-pulped
 
56
Other
 
26
Pulped natural / honey
 
14

Length

Max length25
Median length12
Mean length12.80845557
Min length5

Characters and Unicode

Total characters14845
Distinct characters25
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWashed / Wet
2nd rowWashed / Wet
3rd rowNatural / Dry
4th rowWashed / Wet
5th rowNatural / Dry
ValueCountFrequency (%)
Washed / Wet812
61.9%
Natural / Dry251
 
19.1%
Semi-washed / Semi-pulped56
 
4.3%
Other26
 
2.0%
Pulped natural / honey14
 
1.1%
(Missing)152
 
11.6%
2021-02-14T14:11:43.675724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-14T14:11:43.745867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1133
32.9%
washed812
23.6%
wet812
23.6%
natural265
 
7.7%
dry251
 
7.3%
semi-pulped56
 
1.6%
semi-washed56
 
1.6%
other26
 
0.8%
honey14
 
0.4%
pulped14
 
0.4%

Most occurring characters

ValueCountFrequency (%)
2280
15.4%
e1902
12.8%
W1624
10.9%
a1398
9.4%
/1133
7.6%
t1103
7.4%
d938
6.3%
h908
 
6.1%
s868
 
5.8%
r542
 
3.7%
Other values (15)2149
14.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9042
60.9%
Space Separator2280
 
15.4%
Uppercase Letter2278
 
15.3%
Other Punctuation1133
 
7.6%
Dash Punctuation112
 
0.8%

Most frequent character per category

ValueCountFrequency (%)
e1902
21.0%
a1398
15.5%
t1103
12.2%
d938
10.4%
h908
10.0%
s868
9.6%
r542
 
6.0%
u335
 
3.7%
l335
 
3.7%
y265
 
2.9%
Other values (6)448
 
5.0%
ValueCountFrequency (%)
W1624
71.3%
N251
 
11.0%
D251
 
11.0%
S112
 
4.9%
O26
 
1.1%
P14
 
0.6%
ValueCountFrequency (%)
2280
100.0%
ValueCountFrequency (%)
/1133
100.0%
ValueCountFrequency (%)
-112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11320
76.3%
Common3525
 
23.7%

Most frequent character per script

ValueCountFrequency (%)
e1902
16.8%
W1624
14.3%
a1398
12.3%
t1103
9.7%
d938
8.3%
h908
8.0%
s868
7.7%
r542
 
4.8%
u335
 
3.0%
l335
 
3.0%
Other values (12)1367
12.1%
ValueCountFrequency (%)
2280
64.7%
/1133
32.1%
-112
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII14845
100.0%

Most frequent character per block

ValueCountFrequency (%)
2280
15.4%
e1902
12.8%
W1624
10.9%
a1398
9.4%
/1133
7.6%
t1103
7.4%
d938
6.3%
h908
 
6.1%
s868
 
5.8%
r542
 
3.7%
Other values (15)2149
14.5%

Aroma
Real number (ℝ≥0)

Distinct33
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.563806255
Minimum0
Maximum8.75
Zeros1
Zeros (%)0.1%
Memory size10.4 KiB
2021-02-14T14:11:43.834625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.08
Q17.42
median7.58
Q37.75
95-th percentile8.08
Maximum8.75
Range8.75
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.3786663084
Coefficient of variation (CV)0.05006293071
Kurtosis122.3780996
Mean7.563806255
Median Absolute Deviation (MAD)0.17
Skewness-6.306325975
Sum9916.15
Variance0.1433881731
MonotocityNot monotonic
2021-02-14T14:11:43.934097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
7.67173
13.2%
7.5163
12.4%
7.58149
11.4%
7.75122
9.3%
7.42121
9.2%
7.83101
7.7%
7.3396
7.3%
7.2578
 
5.9%
7.9257
 
4.3%
7.1745
 
3.4%
Other values (23)206
15.7%
ValueCountFrequency (%)
01
0.1%
5.081
0.1%
6.171
0.1%
6.331
0.1%
6.421
0.1%
ValueCountFrequency (%)
8.751
 
0.1%
8.672
 
0.2%
8.581
 
0.1%
8.53
 
0.2%
8.429
0.7%

Flavor
Real number (ℝ≥0)

Distinct35
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.518070175
Minimum0
Maximum8.83
Zeros1
Zeros (%)0.1%
Memory size10.4 KiB
2021-02-14T14:11:44.032217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.92
Q17.33
median7.58
Q37.75
95-th percentile8
Maximum8.83
Range8.83
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.3999792147
Coefficient of variation (CV)0.05320237845
Kurtosis95.17293404
Mean7.518070175
Median Absolute Deviation (MAD)0.17
Skewness-5.223511929
Sum9856.19
Variance0.1599833722
MonotocityNot monotonic
2021-02-14T14:11:44.139517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7.5164
12.5%
7.58162
12.4%
7.67145
11.1%
7.75120
9.2%
7.42114
8.7%
7.33110
8.4%
7.8385
 
6.5%
7.2564
 
4.9%
7.1756
 
4.3%
7.9242
 
3.2%
Other values (25)249
19.0%
ValueCountFrequency (%)
01
 
0.1%
6.081
 
0.1%
6.172
0.2%
6.333
0.2%
6.421
 
0.1%
ValueCountFrequency (%)
8.831
 
0.1%
8.674
0.3%
8.582
 
0.2%
8.55
0.4%
8.425
0.4%

Aftertaste
Real number (ℝ≥0)

Distinct35
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.397696415
Minimum0
Maximum8.67
Zeros1
Zeros (%)0.1%
Memory size10.4 KiB
2021-02-14T14:11:44.241318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.83
Q17.25
median7.42
Q37.58
95-th percentile7.92
Maximum8.67
Range8.67
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.4051186267
Coefficient of variation (CV)0.05476280776
Kurtosis84.64494842
Mean7.397696415
Median Absolute Deviation (MAD)0.17
Skewness-4.845055274
Sum9698.38
Variance0.1641211017
MonotocityNot monotonic
2021-02-14T14:11:44.345055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7.5162
12.4%
7.33150
11.4%
7.42127
9.7%
7.58125
9.5%
7.25103
7.9%
7.6799
 
7.6%
7.1790
 
6.9%
7.7581
 
6.2%
762
 
4.7%
7.8361
 
4.7%
Other values (25)251
19.1%
ValueCountFrequency (%)
01
 
0.1%
6.178
0.6%
6.251
 
0.1%
6.336
0.5%
6.424
0.3%
ValueCountFrequency (%)
8.671
 
0.1%
8.582
0.2%
8.54
0.3%
8.423
0.2%
8.332
0.2%

Acidity
Real number (ℝ≥0)

Distinct31
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.533112128
Minimum0
Maximum8.75
Zeros1
Zeros (%)0.1%
Memory size10.4 KiB
2021-02-14T14:11:44.444957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q17.33
median7.5
Q37.75
95-th percentile8.08
Maximum8.75
Range8.75
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.3815987887
Coefficient of variation (CV)0.0506561939
Kurtosis116.272077
Mean7.533112128
Median Absolute Deviation (MAD)0.17
Skewness-5.967873549
Sum9875.91
Variance0.1456176355
MonotocityNot monotonic
2021-02-14T14:11:44.538763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7.5160
12.2%
7.58150
11.4%
7.67143
10.9%
7.42127
9.7%
7.75122
9.3%
7.33110
8.4%
7.2586
 
6.6%
7.1773
 
5.6%
7.8372
 
5.5%
847
 
3.6%
Other values (21)221
16.9%
ValueCountFrequency (%)
01
0.1%
5.251
0.1%
6.081
0.1%
6.251
0.1%
6.51
0.1%
ValueCountFrequency (%)
8.751
 
0.1%
8.581
 
0.1%
8.57
0.5%
8.426
0.5%
8.339
0.7%

Body
Real number (ℝ≥0)

Distinct31
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.517726926
Minimum0
Maximum8.58
Zeros1
Zeros (%)0.1%
Memory size10.4 KiB
2021-02-14T14:11:44.639177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.08
Q17.33
median7.5
Q37.67
95-th percentile8
Maximum8.58
Range8.58
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.3592129078
Coefficient of variation (CV)0.04778211704
Kurtosis146.9094945
Mean7.517726926
Median Absolute Deviation (MAD)0.17
Skewness-7.15543723
Sum9855.74
Variance0.1290339132
MonotocityNot monotonic
2021-02-14T14:11:44.736663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7.5198
15.1%
7.67149
11.4%
7.58136
10.4%
7.33131
10.0%
7.42125
9.5%
7.75108
8.2%
7.2586
6.6%
7.8382
6.3%
7.1768
 
5.2%
7.9248
 
3.7%
Other values (21)180
13.7%
ValueCountFrequency (%)
01
0.1%
5.251
0.1%
6.332
0.2%
6.421
0.1%
6.51
0.1%
ValueCountFrequency (%)
8.581
 
0.1%
8.53
0.2%
8.423
0.2%
8.336
0.5%
8.255
0.4%

Balance
Real number (ℝ≥0)

Distinct32
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.517505721
Minimum0
Maximum8.75
Zeros1
Zeros (%)0.1%
Memory size10.4 KiB
2021-02-14T14:11:44.833630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.92
Q17.33
median7.5
Q37.75
95-th percentile8.08
Maximum8.75
Range8.75
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.4063159224
Coefficient of variation (CV)0.05404930006
Kurtosis89.1182623
Mean7.517505721
Median Absolute Deviation (MAD)0.17
Skewness-4.844280103
Sum9855.45
Variance0.1650926288
MonotocityNot monotonic
2021-02-14T14:11:44.935144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
7.5172
13.1%
7.67145
11.1%
7.58127
9.7%
7.42120
9.2%
7.75103
 
7.9%
7.3399
 
7.6%
7.8398
 
7.5%
7.1771
 
5.4%
7.2564
 
4.9%
746
 
3.5%
Other values (22)266
20.3%
ValueCountFrequency (%)
01
 
0.1%
6.081
 
0.1%
6.173
0.2%
6.331
 
0.1%
6.421
 
0.1%
ValueCountFrequency (%)
8.752
 
0.2%
8.587
0.5%
8.57
0.5%
8.427
0.5%
8.337
0.5%

Uniformity
Real number (ℝ≥0)

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.833394355
Minimum0
Maximum10
Zeros1
Zeros (%)0.1%
Memory size10.4 KiB
2021-02-14T14:11:45.021862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.67
Q110
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5593431229
Coefficient of variation (CV)0.05688199849
Kurtosis84.1523048
Mean9.833394355
Median Absolute Deviation (MAD)0
Skewness-6.926117322
Sum12891.58
Variance0.3128647291
MonotocityNot monotonic
2021-02-14T14:11:45.108833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
101128
86.0%
9.33112
 
8.5%
8.6731
 
2.4%
825
 
1.9%
6.677
 
0.5%
63
 
0.2%
7.332
 
0.2%
01
 
0.1%
91
 
0.1%
9.51
 
0.1%
ValueCountFrequency (%)
01
 
0.1%
63
 
0.2%
6.677
 
0.5%
7.332
 
0.2%
825
1.9%
ValueCountFrequency (%)
101128
86.0%
9.51
 
0.1%
9.33112
 
8.5%
91
 
0.1%
8.6731
 
2.4%

Clean.Cup
Real number (ℝ≥0)

Distinct11
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.833119756
Minimum0
Maximum10
Zeros2
Zeros (%)0.2%
Memory size10.4 KiB
2021-02-14T14:11:45.190232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.33
Q110
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7713497341
Coefficient of variation (CV)0.07844404963
Kurtosis69.17317576
Mean9.833119756
Median Absolute Deviation (MAD)0
Skewness-7.377824205
Sum12891.22
Variance0.5949804124
MonotocityNot monotonic
2021-02-14T14:11:45.275379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
101194
91.1%
9.3358
 
4.4%
8.6716
 
1.2%
813
 
1.0%
6.6713
 
1.0%
66
 
0.5%
5.333
 
0.2%
7.333
 
0.2%
02
 
0.2%
2.672
 
0.2%
ValueCountFrequency (%)
02
 
0.2%
1.331
 
0.1%
2.672
 
0.2%
5.333
0.2%
66
0.5%
ValueCountFrequency (%)
101194
91.1%
9.3358
 
4.4%
8.6716
 
1.2%
813
 
1.0%
7.333
 
0.2%

Sweetness
Real number (ℝ≥0)

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.903272311
Minimum0
Maximum10
Zeros1
Zeros (%)0.1%
Memory size10.4 KiB
2021-02-14T14:11:45.355179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.33
Q110
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.530831741
Coefficient of variation (CV)0.05360165048
Kurtosis157.5282853
Mean9.903272311
Median Absolute Deviation (MAD)0
Skewness-10.75633154
Sum12983.19
Variance0.2817823372
MonotocityNot monotonic
2021-02-14T14:11:45.437761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
101218
92.9%
9.3361
 
4.7%
8.6712
 
0.9%
88
 
0.6%
6.677
 
0.5%
63
 
0.2%
01
 
0.1%
1.331
 
0.1%
ValueCountFrequency (%)
01
 
0.1%
1.331
 
0.1%
63
 
0.2%
6.677
0.5%
88
0.6%
ValueCountFrequency (%)
101218
92.9%
9.3361
 
4.7%
8.6712
 
0.9%
88
 
0.6%
6.677
 
0.5%

Cupper.Points
Real number (ℝ≥0)

Distinct42
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.497864226
Minimum0
Maximum10
Zeros1
Zeros (%)0.1%
Memory size10.4 KiB
2021-02-14T14:11:45.535151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.83
Q17.25
median7.5
Q37.75
95-th percentile8.08
Maximum10
Range10
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.4746100206
Coefficient of variation (CV)0.06329936183
Kurtosis50.15643051
Mean7.497864226
Median Absolute Deviation (MAD)0.25
Skewness-2.83887447
Sum9829.7
Variance0.2252546716
MonotocityNot monotonic
2021-02-14T14:11:45.656003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
7.5152
11.6%
7.58136
10.4%
7.33114
 
8.7%
7.67113
 
8.6%
7.42103
 
7.9%
7.2585
 
6.5%
7.7584
 
6.4%
7.8381
 
6.2%
7.1763
 
4.8%
7.9252
 
4.0%
Other values (32)328
25.0%
ValueCountFrequency (%)
01
0.1%
5.171
0.1%
5.251
0.1%
5.421
0.1%
61
0.1%
ValueCountFrequency (%)
104
0.3%
9.251
 
0.1%
91
 
0.1%
8.831
 
0.1%
8.751
 
0.1%

Total.Cup.Points
Real number (ℝ≥0)

Distinct178
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.11592677
Minimum0
Maximum90.58
Zeros1
Zeros (%)0.1%
Memory size10.4 KiB
2021-02-14T14:11:45.779986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.92
Q181.17
median82.5
Q383.67
95-th percentile85.5
Maximum90.58
Range90.58
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation3.515760681
Coefficient of variation (CV)0.04281460149
Kurtosis229.2566405
Mean82.11592677
Median Absolute Deviation (MAD)1.25
Skewness-10.52961736
Sum107653.98
Variance12.36057317
MonotocityDecreasing
2021-02-14T14:11:45.897700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.1738
 
2.9%
8337
 
2.8%
82.4231
 
2.4%
82.3329
 
2.2%
82.7529
 
2.2%
82.6726
 
2.0%
82.9226
 
2.0%
81.8326
 
2.0%
81.6725
 
1.9%
83.2524
 
1.8%
Other values (168)1020
77.8%
ValueCountFrequency (%)
01
0.1%
59.831
0.1%
63.081
0.1%
67.921
0.1%
68.331
0.1%
ValueCountFrequency (%)
90.581
0.1%
89.921
0.1%
89.751
0.1%
891
0.1%
88.832
0.2%

Moisture
Real number (ℝ≥0)

ZEROS

Distinct23
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08886346301
Minimum0
Maximum0.28
Zeros252
Zeros (%)19.2%
Memory size10.4 KiB
2021-02-14T14:11:46.005183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.09
median0.11
Q30.12
95-th percentile0.13
Maximum0.28
Range0.28
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.0479567757
Coefficient of variation (CV)0.539668094
Kurtosis-0.09626774409
Mean0.08886346301
Median Absolute Deviation (MAD)0.01
Skewness-1.010997245
Sum116.5
Variance0.002299852336
MonotocityNot monotonic
2021-02-14T14:11:46.100133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.11381
29.1%
0.12284
21.7%
0252
19.2%
0.1180
13.7%
0.1375
 
5.7%
0.0926
 
2.0%
0.1423
 
1.8%
0.0816
 
1.2%
0.0115
 
1.1%
0.058
 
0.6%
Other values (13)51
 
3.9%
ValueCountFrequency (%)
0252
19.2%
0.0115
 
1.1%
0.027
 
0.5%
0.034
 
0.3%
0.044
 
0.3%
ValueCountFrequency (%)
0.281
 
0.1%
0.221
 
0.1%
0.211
 
0.1%
0.23
0.2%
0.182
0.2%

Category.One.Defects
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4263920671
Minimum0
Maximum31
Zeros1111
Zeros (%)84.7%
Memory size10.4 KiB
2021-02-14T14:11:46.186531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum31
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.832415257
Coefficient of variation (CV)4.297489092
Kurtosis142.6271965
Mean0.4263920671
Median Absolute Deviation (MAD)0
Skewness10.24033867
Sum559
Variance3.357745675
MonotocityNot monotonic
2021-02-14T14:11:46.273838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
01111
84.7%
1101
 
7.7%
238
 
2.9%
318
 
1.4%
416
 
1.2%
59
 
0.7%
104
 
0.3%
63
 
0.2%
73
 
0.2%
312
 
0.2%
Other values (6)6
 
0.5%
ValueCountFrequency (%)
01111
84.7%
1101
 
7.7%
238
 
2.9%
318
 
1.4%
416
 
1.2%
ValueCountFrequency (%)
312
0.2%
231
0.1%
151
0.1%
121
0.1%
111
0.1%

Quakers
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)0.8%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.1770992366
Minimum0
Maximum11
Zeros1216
Zeros (%)92.8%
Memory size10.4 KiB
2021-02-14T14:11:46.357934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8405830206
Coefficient of variation (CV)4.746395504
Kurtosis57.04544909
Mean0.1770992366
Median Absolute Deviation (MAD)0
Skewness6.860056999
Sum232
Variance0.7065798144
MonotocityNot monotonic
2021-02-14T14:11:46.441205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
01216
92.8%
139
 
3.0%
230
 
2.3%
35
 
0.4%
55
 
0.4%
45
 
0.4%
64
 
0.3%
73
 
0.2%
91
 
0.1%
111
 
0.1%
ValueCountFrequency (%)
01216
92.8%
139
 
3.0%
230
 
2.3%
35
 
0.4%
45
 
0.4%
ValueCountFrequency (%)
111
 
0.1%
91
 
0.1%
81
 
0.1%
73
0.2%
64
0.3%

Category.Two.Defects
Real number (ℝ≥0)

ZEROS

Distinct38
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.591914569
Minimum0
Maximum55
Zeros362
Zeros (%)27.6%
Memory size10.4 KiB
2021-02-14T14:11:46.532936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile13
Maximum55
Range55
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.350370915
Coefficient of variation (CV)1.489559624
Kurtosis19.81773785
Mean3.591914569
Median Absolute Deviation (MAD)2
Skewness3.648565295
Sum4709
Variance28.62646893
MonotocityNot monotonic
2021-02-14T14:11:46.640087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0362
27.6%
1200
15.3%
2178
13.6%
3133
 
10.1%
4118
 
9.0%
573
 
5.6%
642
 
3.2%
739
 
3.0%
829
 
2.2%
922
 
1.7%
Other values (28)115
 
8.8%
ValueCountFrequency (%)
0362
27.6%
1200
15.3%
2178
13.6%
3133
 
10.1%
4118
 
9.0%
ValueCountFrequency (%)
551
0.1%
471
0.1%
451
0.1%
401
0.1%
381
0.1%

altitude_low_meters
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct188
Distinct (%)17.3%
Missing227
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean1759.548954
Minimum1
Maximum190164
Zeros0
Zeros (%)0.0%
Memory size10.4 KiB
2021-02-14T14:11:46.748198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile442
Q11100
median1310.64
Q31600
95-th percentile1850
Maximum190164
Range190163
Interquartile range (IQR)500

Descriptive statistics

Standard deviation8767.847252
Coefficient of variation (CV)4.983008419
Kurtosis414.4721807
Mean1759.548954
Median Absolute Deviation (MAD)239.36
Skewness20.0978744
Sum1907351.066
Variance76875145.44
MonotocityNot monotonic
2021-02-14T14:11:46.869504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120080
 
6.1%
160065
 
5.0%
140059
 
4.5%
110055
 
4.2%
150054
 
4.1%
130048
 
3.7%
180041
 
3.1%
125038
 
2.9%
170036
 
2.7%
155031
 
2.4%
Other values (178)577
44.0%
(Missing)227
 
17.3%
ValueCountFrequency (%)
114
1.1%
123
 
0.2%
132
 
0.2%
501
 
0.1%
1002
 
0.2%
ValueCountFrequency (%)
1901642
0.2%
1100001
0.1%
110001
0.1%
42871
0.1%
40011
0.1%

altitude_high_meters
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct188
Distinct (%)17.3%
Missing227
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean1808.843803
Minimum1
Maximum190164
Zeros0
Zeros (%)0.0%
Memory size10.4 KiB
2021-02-14T14:11:46.989041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile442
Q11100
median1350
Q31650
95-th percentile1950
Maximum190164
Range190163
Interquartile range (IQR)550

Descriptive statistics

Standard deviation8767.187498
Coefficient of variation (CV)4.846846081
Kurtosis414.1442992
Mean1808.843803
Median Absolute Deviation (MAD)250
Skewness20.08565748
Sum1960786.682
Variance76863576.63
MonotocityNot monotonic
2021-02-14T14:11:47.110685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120065
 
5.0%
140063
 
4.8%
110054
 
4.1%
150051
 
3.9%
180044
 
3.4%
130044
 
3.4%
170042
 
3.2%
125039
 
3.0%
160034
 
2.6%
195033
 
2.5%
Other values (178)615
46.9%
(Missing)227
 
17.3%
ValueCountFrequency (%)
112
0.9%
123
 
0.2%
132
 
0.2%
501
 
0.1%
1001
 
0.1%
ValueCountFrequency (%)
1901642
0.2%
1100001
0.1%
110001
0.1%
59001
0.1%
42871
0.1%

altitude_mean_meters
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct201
Distinct (%)18.5%
Missing227
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean1784.196379
Minimum1
Maximum190164
Zeros0
Zeros (%)0.0%
Memory size10.4 KiB
2021-02-14T14:11:47.226800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile442
Q11100
median1310.64
Q31600
95-th percentile1880
Maximum190164
Range190163
Interquartile range (IQR)500

Descriptive statistics

Standard deviation8767.016913
Coefficient of variation (CV)4.913706259
Kurtosis414.4034975
Mean1784.196379
Median Absolute Deviation (MAD)239.36
Skewness20.09518078
Sum1934068.874
Variance76860585.55
MonotocityNot monotonic
2021-02-14T14:11:47.344886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120066
 
5.0%
110052
 
4.0%
140052
 
4.0%
130050
 
3.8%
150044
 
3.4%
125039
 
3.0%
170036
 
2.7%
160035
 
2.7%
155034
 
2.6%
175034
 
2.6%
Other values (191)642
49.0%
(Missing)227
 
17.3%
ValueCountFrequency (%)
112
0.9%
123
 
0.2%
132
 
0.2%
501
 
0.1%
1001
 
0.1%
ValueCountFrequency (%)
1901642
0.2%
1100001
0.1%
110001
0.1%
42871
0.1%
40011
0.1%

Interactions

2021-02-14T14:11:08.917696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:09.075783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:09.182724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:09.269690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:09.355928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:09.442542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:09.529364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:09.616022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:09.703169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:09.789792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:09.877810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:09.962645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:10.049301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:10.138185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:10.328646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:10.417396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:10.510651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:10.603479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:10.700968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:10.784763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:10.865425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:10.947908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:11.028149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:11.108689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:11.191057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:11.274859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:11.358004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:11.442360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:11.526842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:11.609357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:11.694584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:11.780800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:11.879540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:11.987612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:12.107124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:12.228629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:12.354250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:12.466180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:12.577087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:12.679618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:12.761142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:12.846441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:12.932462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:13.019560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:13.106683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:13.194029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:13.281651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:13.476185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:13.570067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:13.671517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:13.766024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:13.856362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:13.948655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:14.043182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:14.141229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:14.232308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:14.317372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:14.399544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:14.483981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:14.573740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:14.659055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:14.750145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:14.839868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:14.923829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:15.010323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:15.099102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:15.184976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:15.273100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:15.360129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:15.448301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:15.543565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:15.659088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:15.764590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:15.858447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:15.963597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:16.057112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:16.146145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:16.231278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:16.314706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:16.399908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:16.485768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:16.570124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:16.656467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:16.742363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:16.847837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:16.942283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:17.034328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:17.255678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:17.394361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:17.501706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:17.603178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:17.696109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:17.780778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:17.863922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:17.948467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:18.034241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:18.119778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:18.206320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:18.292675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:18.381926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:18.470119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:18.558286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:18.648490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:18.741821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:18.841339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:18.970806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:19.065323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:19.156814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:19.250682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:19.342230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:19.434953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:19.520153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:19.618461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:19.702709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:19.790326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:19.885169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:19.974523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:20.066485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:20.171771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:20.259629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:20.349855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:20.442304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:20.530707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:20.619032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:20.711592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:20.806936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:20.896974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:20.990675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:21.080151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:21.170454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:21.260322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:21.351336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:21.434208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:21.521666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:21.606928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:21.694701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:21.779204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:22.050670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:22.155086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:22.250927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:22.343290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:22.437864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:22.536406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:22.630089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:22.730641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:22.824244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:22.910476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:22.993921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:23.084940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:23.174942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:23.259630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:23.370632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:23.468627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:23.565861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:23.656782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:23.742962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:23.843153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:23.948257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:24.034793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:24.121037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:24.216704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:24.321503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:24.443148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:24.541963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:24.638075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:24.726004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:24.826286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:24.918642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:25.010161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:25.104916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:25.202743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:25.299224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:25.399764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:25.492012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:25.586576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:25.678620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:25.769334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:25.856906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:25.946696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:26.035829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:26.127497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:26.213797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:26.311157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:26.403835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:26.496758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:26.593497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:26.687763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:26.791763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:26.903408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:27.000907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:27.091906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:27.180721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:27.271456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:27.371150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:27.463928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:27.550780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:27.653969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:27.993642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:28.106098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:28.205463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:28.302131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:28.396306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:28.494240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:28.591583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:28.688643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:28.783811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:28.880619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:28.980175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:29.079511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:29.178575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:29.270107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:29.356011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:29.441474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:29.526740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:29.664710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:29.775742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:29.887810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:29.980658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:30.070823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:30.159352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:30.244039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:30.329497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:30.413228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:30.495871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:30.584455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:30.677882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:30.763295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:30.849676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:30.934347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:31.021646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:31.111747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:31.197141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:31.294653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:31.392776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:31.491014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:31.582233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:31.669254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:31.755200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:31.841183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:31.927605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:32.012586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:32.101381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:32.189047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:32.276672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:32.366858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:32.455283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:32.541437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:32.633961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:32.721423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:32.810315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:32.904287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:33.001236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-14T14:11:33.710300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-14T14:11:33.968953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:34.057061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-14T14:11:34.265489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:34.368964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:34.457446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:34.550629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:34.654786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-14T14:11:35.104106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:35.197020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:35.281665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:35.366959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:35.451704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:35.536003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:35.629739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:35.716396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:35.801425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-14T14:11:35.973075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-14T14:11:37.577899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:37.668013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-14T14:11:38.044262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-14T14:11:40.423556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:40.512047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:40.606309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-14T14:11:40.788427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:40.879567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:40.968462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:41.068139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:41.162668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:41.253276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:41.342706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T14:11:41.437820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-02-14T14:11:47.470378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-14T14:11:47.664435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-14T14:11:47.843625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-14T14:11:48.028414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-14T14:11:48.179105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-14T14:11:41.683337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-14T14:11:41.933119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-14T14:11:42.167841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-14T14:11:42.302213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Country of OriginProducerNumber.of.BagsProcessing MethodAromaFlavorAftertasteAcidityBodyBalanceUniformityClean.CupSweetnessCupper.PointsTotal.Cup.PointsMoistureCategory.One.DefectsQuakersCategory.Two.Defectsaltitude_low_metersaltitude_high_metersaltitude_mean_meters
0EthiopiaMETAD PLC300Washed / Wet8.678.838.678.758.508.4210.0010.010.008.7590.580.1200.001950.02200.02075.0
1EthiopiaMETAD PLC300Washed / Wet8.758.678.508.588.428.4210.0010.010.008.5889.920.1200.011950.02200.02075.0
2GuatemalaNaN5NaN8.428.508.428.428.338.4210.0010.010.009.2589.750.0000.001600.01800.01700.0
3EthiopiaYidnekachew Dabessa Coffee Plantation320Natural / Dry8.178.588.428.428.508.2510.0010.010.008.6789.000.1100.021800.02200.02000.0
4EthiopiaMETAD PLC300Washed / Wet8.258.508.258.508.428.3310.0010.010.008.5888.830.1200.021950.02200.02075.0
5BrazilNaN100Natural / Dry8.588.428.428.508.258.3310.0010.010.008.3388.830.1100.01NaNNaNNaN
6PeruHVC100Washed / Wet8.428.508.338.508.258.2510.0010.010.008.5088.750.1100.00NaNNaNNaN
7EthiopiaBazen Agricultural & Industrial Dev't Plc300NaN8.258.338.508.428.338.5010.0010.09.339.0088.670.0300.001570.01700.01635.0
8EthiopiaBazen Agricultural & Industrial Dev't Plc300NaN8.678.678.588.428.338.429.3310.09.338.6788.420.0300.001570.01700.01635.0
9EthiopiaDiamond Enterprise Plc50Natural / Dry8.088.588.508.507.678.4210.0010.010.008.5088.250.1000.041795.01850.01822.5

Last rows

Country of OriginProducerNumber.of.BagsProcessing MethodAromaFlavorAftertasteAcidityBodyBalanceUniformityClean.CupSweetnessCupper.PointsTotal.Cup.PointsMoistureCategory.One.DefectsQuakersCategory.Two.Defectsaltitude_low_metersaltitude_high_metersaltitude_mean_meters
1301Mexicovarious small producers280Washed / Wet6.927.006.836.927.426.926.006.0010.006.7570.750.1200.011000.001000.001000.00
1302BrazilNaN305Natural / Dry7.007.006.837.007.336.836.006.0010.006.6770.670.1101.055NaNNaNNaN
1303HondurasOmar Acosta275Washed / Wet6.676.506.176.676.836.178.008.008.006.3369.330.1000.041450.001450.001450.00
1304HondurasOmar Acosta275Washed / Wet7.006.176.176.676.506.178.008.008.006.5069.170.1000.031450.001450.001450.00
1305HondurasOmar Acosta275Washed / Wet7.006.336.176.506.676.178.008.008.006.3369.170.1000.041450.001450.001450.00
1306MexicoJUAN CARLOS GARCÍA LOPEZ12Washed / Wet7.086.836.257.427.256.7510.000.0010.006.7568.330.1100.020900.00900.00900.00
1307HaitiCOEB Koperativ Ekselsyo Basen1Natural / Dry6.756.586.426.677.086.679.336.006.006.4267.920.1480.016350.00350.00350.00
1308NicaraguaTeófilo Narváez550Other7.256.586.336.256.426.086.006.006.006.1763.080.1310.051100.001100.001100.00
1309GuatemalaWILLIAM ESTUARDO MARTINEZ PACHECO275Washed / Wet7.506.676.677.677.336.678.001.331.336.6759.830.1000.041417.321417.321417.32
1310HondurasReinerio Zepeda275NaN0.000.000.000.000.000.000.000.000.000.000.000.1200.021400.001400.001400.00